Posterior distribution of two parameters with discrete priors

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Description

Computes the posterior distribution for an arbitrary two parameter distribution for a discrete prior distribution.

Usage

1

Arguments

df

name of the function defining the sampling density of two parameters

prior

matrix defining the prior density; the row names and column names of the matrix define respectively the values of parameter 1 and values of parameter 2 and the entries of the matrix give the prior probabilities

y

y is a matrix of data values, where each row corresponds to a single observation

...

any further fixed parameter values used in the sampling density function

Value

prob

matrix of posterior probabilities

pred

scalar with prior predictive probability

Author(s)

Jim Albert

Examples

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p1 = seq(0.1, 0.9, length = 9)
p2 = p1
prior = matrix(1/81, 9, 9)
dimnames(prior)[[1]] = p1
dimnames(prior)[[2]] = p2
discrete.bayes.2(twoproplike,prior)

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